AI Limitations and Future Directions
Proving generalizability and real-world applications
While AI is rapidly being incorporated into oncologic research, work remains to be done to translate these studies into real-world, clinically meaningful applications. One of the biggest barriers is in external validation and proving generalizability of DL applications. Given the complexity of neural networks and the extremely large number of parameters (often in the millions), there is a high tendency for neural networks to create overfitted models that do not generalize across different populations. Additionally, because there is a significant amount of heterogeneity of medical data across institutions, multiple external validation sets may be required to prove the performance of an application.
Data access and equity
Directly contributing to this problem of overfitting are limitations with data access and quality. DL neural networks, more than any other ML algorithm, require large amounts of data. This can pose a problem in healthcare when attempting to apply AI to disease processes with less prevalence. Furthermore, data is often siloed within individual institutions. Contributing to this relative data drought are concerns with transmission of protected patient health information, along with lack of data-sharing infrastructure to link institutions, heterogeneity and incompleteness in the collection of data, and competition between institutions. These obstacles are beginning to be addressed, with more and more emphasis on streamlined data capture, and a number of multi-institutional data-sharing agreements[58-61] Guidelines have been proposed to support FAIR (findable, accessible, interoperable, reusable) data use, and there are now opportunities for research groups to publish their data itself, which may incentivize openness.
Interpretability and the black box problem
One of the central limitations to adoption of AI in healthcare is the concern that these models, despite regularly achieving high performance, are somewhat opaque. For instance, a DL model may correctly predict that a patient will develop pancreatic cancer based on his past 2 years of EHR data, but why did it make that prediction? At the present, we are limited in our ability to determine the precise logic behind DL-based predictions. This is often referred to as the “black box” problem. In medical practice, it has traditionally been essential in clinical decision-making to know the rationale for each decision. Traditional ML algorithms, like linear regression, have limited ability to model complex relationships, but offer this easy interpretability—in these algorithms, we have a set of pre-defined features and the resulting feature weights that characterize their effect sizes. In contrast, DL utilizes unstructured input data, and the bulk of knowledge generation occurs within the hidden layers. It thus becomes difficult to determine which specific characteristic(s) of the input data contributed to the outcome. This interpretability challenge has large implications for adoption of AI-based algorithms in healthcare, both from practitioner and regulatory perspectives.[65-68]
Tackling the black box problem has now become a major focus of research. In AI image analysis algorithms, several methods have been developed, including feature visualization, saliency maps, class activation mapping, and sensitivity analyses, where certain parts of the image are hidden to the effect on prediction. While these methods have advanced over the past few years, further work is needed to better elucidate the decision-making logic with deep neural networks.
Education and expertise
To successfully merge AI with clinical oncology and maximize its impact, there are knowledge gaps that need to be addressed. Currently, physicians receive little training in data science and ML, limiting their ability to understand DL mechanisms, adopt algorithms appropriately, and conduct research. Similarly, most data scientists have little experience with oncologic workup and management, limiting the ability to identify important and suitable clinical use cases. Further collaboration should be pursued between clinical oncologic departments and bioinformatics and data science divisions, and strategic partnerships with technology firms should be formed where appropriate.
Promoting AI in Oncology: Professional Societies and National Initiatives
In response to these challenges, several national professional societies have launched initiatives to bridge these knowledge gaps and promote the dissemination of AI in oncology.
American College of Radiology (ACR)
The ACR has founded a Data Science Institute (ACR-DSI) with the mission of collaborating with radiologists, industry, and government agencies to facilitate the development of AI in imaging. Within the ACR-DSI are several core goals: 1) providing standards for measuring performance of AI algorithms (“Touch-AI”), 2) independent, external validation of algorithms and navigating the regulatory landscape (“Certify-AI”), and longitudinal, prospective evaluation of deployed algorithm performance “(Assess-AI”). The ACR-DSI has additionally set up a series of use cases for recommended AI imaging applications with unmet clinical need.
American Society of Clinical Oncology (ASCO) and American Society for Radiation Oncology (ASTRO)
ASCO has launched a big data initiative named CancerLinQ, in partnership with oncologists, industry, and academia, with the goals of real-time quality of care tracking and treatment evaluation, as well as knowledge dissemination to oncologists in user-friendly ways. The initiative’s backbone is a constantly growing database of de-identified patient information that can be mined and analyzed. In 2017, ASTRO partnered with CancerLinQ to provide radiation oncology expertise and uses for the database. In addition, Big Data Analytics and Bioinformatics is one of the core initiatives of the ASTRO Research Agenda for 2018.
National Institutes of Health (NIH)
As part of the NIH Common Fund, the Big Data to Knowledge (BD2K) initiative was launched to support the research and development of tools to integrate big data and data science into biomedical research. One of the central components of the initiative involves leveraging existing national datasets, such as the Library of Integrated Network-based Cellular Signatures (LINCS) and The Cancer Genome Atlas (TCGA), and applying ML techniques to discover patterns in the data that may result in heretofore unknown compounds for cancer therapeutics.
Over the past decade, AI has undergone a reawakening. Due to an explosion of electronic data, advances in technological infrastructure, and groundbreaking research in DL neural networks, AI is poised to make practice-changing impacts on the medical field and oncologic care. At present, AI has shown promise in improving cancer imaging diagnostics and treatment response evaluation, predicting clinical outcomes, and catalyzing drug development and translational oncology. Obstacles remain—such as validation and proving generalizability, concerns over interpretation, and the widening knowledge gap between clinical and data science experts. If we can address these challenges, AI has the potential to transform oncology, harnessing the power of big data to drive cancer care into the 21st century and beyond.
Financial Disclosure: The authors have no significant financial interest in or other relationship with the manufacturer of any product or provider of any service mentioned in this article.
Tufia C. Haddad, MD
Developing applications of artificial intelligence (AI) and cognitive systems in oncology requires a collaborative, multidisciplinary effort that extends far beyond medicine and computer science. More than a few significant challenges, however, limit the translation of AI-related cancer research into meaningful clinical applications. Among these are the abilities to form partnerships across multiple industries, to gain equitable access to large volumes of annotated data, and to conduct unbiased training of machine-learning algorithms. The article by Aneja and colleagues provides a detailed overview of the evolving field of AI in oncology and serves as a primer for curious and reluctant oncologists alike, the vast majority of whom have not received formal training in data science.
While there has been hype that AI has the potential to replace doctors, early experience has suggested that AI can instead augment human intelligence, enabling doctors to perform with greater efficiency, engagement, and effectiveness. That said, a more credible, contemporary threat faces the oncology profession: unprecedented levels of burnout. Although multiple factors have been associated with this state of stress, the ubiquitous involvement of computers in all aspects of medicine is recognized as a major driver. For example, while electronic health records (EHR), computerized prescribing, and order entry were established to improve quality and coordination of patient care, implementation of these systems has resulted in unintended consequences for providers: reduced efficiency and increased clerical and cognitive burden. During office hours, physicians spend almost 2 hours engaged in EHR and desk work for every 1 hour of direct patient face time, and it is estimated that an additional 1.4 hours of EHR interactions occur daily outside of business hours.
The EHR contains an enormous volume of data, and new data sources have the potential to generate a tsunami of additional patient data points. These include tumor genome sequencing reports, electronic patient-reported outcomes responses, patient online messaging, and patient-generated health data from apps and wearables. Integration of these data within the EHR and clinical workflows is currently suboptimal or lacking for most practices, furthering the rift between providers and their computers. Concurrent with this explosion of data is the frenzied pace of knowledge gains in oncology, including a record of 51 new agents or new indications for existing agents that were approved by the US Food and Drug Administration for cancer treatment in 2018.
It has been recognized that much of the existing digital workload can be delegated to other care team members or to data abstraction and documentation specialists. Alternatively, technology may be exactly what is needed to solve our technology problem. Specifically, natural language processing (NLP), a branch of AI, can interpret, augment, and transform free text so that it can be represented for computation. In medicine, it leverages the EHR, including its most valuable asset: the clinical note. NLP serves as the engine for cognitive systems, but adoption of this technology requires expert training and supervision, as well as thoughtful implementation, with minimal disruption to clinical workflows.
Under these conditions, AI offers the potential for detailed EHR summarization in a single click, as well as clinical decision support at the point of care aligned with evidence-based care pathways. AI may also generate order sets, cancer registries, or personalized care plans for treatment or survivorship. Finally, it may provide optimized billing codes and quality/outcomes reporting for programmatic assessments and regulatory mandates. While extraordinary investments have aimed to create AI applications for cancer risk prediction, diagnosis, and treatment, perhaps an equally noble goal would be to develop the NLP capabilities of cognitive systems to eradicate the growing mindless hours spent navigating the EHR, thereby allowing oncologists to do what they do best: provide the highest level of cutting-edge, patient-centered care to those facing cancer.
FINANCIAL DISCLOSURE: Dr. Haddad has no significant financial interest in or other relationship with the manufacturer of any product or provider of any service mentioned in this article.
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Dr. Haddad is a Medical Oncologist, Chair of the Breast Medical Oncology practice, and Chair of Health Information Technology in the Department of Oncology at the Mayo Clinic in Rochester, Minnesota.
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